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Nash Equilibrium In Game Theory ~xRay Pixy

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 Video Link  CLICK HERE... Learn Nash Equilibrium In Game Theory Step-By-Step Using Examples. Video Chapters: Nash Equilibrium  00:00 Introduction 00:19 Topics Covered 00:33 Nash Equilibrium  01:55 Example 1  02:30 Example 2 04:46 Game Core Elements 06:41 Types of Game Strategies 06:55  Prisoner’s Dilemma  07:17  Prisoner’s Dilemma Example 3 09:16 Dominated Strategy  10:56 Applications 11:34 Conclusion The Nash Equilibrium is a concept in game theory that describes a situation where no player can benefit by changing their strategy while the other players keep their strategies unchanged.  No player can increase their payoff by changing their choice alone while others keep theirs the same. Example : If Chrysler, Ford, and GM each choose their production levels so that no company can make more money by changing their choice, it’s a Nash Equilibrium Prisoner’s Dilemma : Two criminals are arrested and interrogated separately. Each has two ...
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Squirrel Search Algorithm (SSA) || STEP - BY - STEP || ~xRay Pixy

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Squirrel Search Algorithm (SSA) Learn Squirrel Search Optimization Algorithm Step-By-Step with Example. Video Chapters: Introduction: 00:00 Squirrel Search Algorithm: 01:11 Squirrel Search Algorithm MODEL: 03:33 Squirrel Search Algorithm STEPS: 06:18 Squirrel Search Algorithm MATHEMATICAL MODELS: 06:26 Conclusion: 15:37
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Optimal Wind Turbine Placement Using Particle Swarm Optimization

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Wind Turbine Optimal Positioning using Particle Swarm Optimization Algorithm Video Chapters: Introduction: 00:00 Wind Energy Projects Objectives: 01:15 Wind Turbine: 04:16 Wind Farm: 05:20 Jensen Wake Effect Model: 06:55 Wind Farm Layout: 09:05 3 Scenarios for Optimal Wind Turbine Positions: 12:02 Metaheuristics for Wind Energy Optimization: 13:54 Optimal Wind Turbine Placement Using Particle Swarm Optimization: 14:53 Optimization Process Flowchart: 20:08 Conclusion: 21:00
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All Members-Based Optimizer (AMBO) || STEP-BY-STEP || ~xRay Pixy

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All Members-Based Optimizer (AMBO) Learn All Members-Based Optimizer Step-by-Step with Examples. Algorithm Type: Metaheuristic Optimization Technique Algorithm Main Idea: Make more use of the Population Matrix. Tested on Different Benchmark Test Functions. Algorithm Performance: Provide Better results in comparison with different metaheuristic optimization algorithms. Used for Solving Optimization Problems. ALGORITHM MAIN IDEA Make use of the Population Matrix and All Members can play role in Updating Algorithm Population. ALL MEMBERS-BASED OPTIMIZER STEPS STEP 01: Initialize Algorithm Important Parameters. STEP 02: Initialize Population Randomly in the Search Space. STEP 03: Evaluate Initial Population using the Fitness Function. STEP 04: Check While (Current Iteration < Maximum Iteration) Do STEP 05: Update Members Position and Best Member Position. STEP 06: Update Population Members using STAGE 01. STEP 07: Update Population Members using STAGE 02. STEP 08: Save Best Solut...
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Elephant Herding Optimization Algorithm || STEP-BY-STEP || ~xRay Pixy

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Elephant Herding Optimization Algorithm Learn Elephant Herding Optimization Algorithm Step-By-Step with Examples. Elephant Herding Optimization Algorithm - Introduced in 2015 - Inspired by Elephant Herding Behavior. - Main Operator used: + Elephant Clan Updating Operator + Elephant Separating Operator - Used to Solve Optimization Problems.
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